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Thermal behavior analysis of a pouch type Li[Ni0.7Co0.15Mn0.15]O2-based lithium-ion battery
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  • 作者:Feng-Ling Yun ; Ling Tang ; Wen-Cheng Li ; Wei-Ren Jin ; Jing Pang ; Shi-Gang Lu
  • 关键词:Lithium ; ion battery ; Thermal behavior ; Li[Ni0.7Co0.15Mn0.15]O2 ; High specific energy
  • 刊名:Rare Metals
  • 出版年:2016
  • 出版时间:April 2016
  • 年:2016
  • 卷:35
  • 期:4
  • 页码:309-319
  • 全文大小:2,506 KB
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  • 作者单位:Feng-Ling Yun (1)
    Ling Tang (1)
    Wen-Cheng Li (1)
    Wei-Ren Jin (1)
    Jing Pang (1)
    Shi-Gang Lu (1)

    1. General Research Institute for Nonferrous Metals, R&D Center for Vehicle Battery and Energy Storage, Beijing, 100088, China
  • 刊物类别:Chemistry and Materials Science
  • 刊物主题:Chemistry
    Metallic Materials
    Chinese Library of Science
  • 出版者:Journal Publishing Center of University of Science and Technology Beijing, in co-publication with Sp
  • ISSN:1867-7185
文摘
Since lithium-ion battery with high energy density is the key component for next-generation electrical vehicles, a full understanding of its thermal behaviors at different discharge rates is quite important for the design and thermal management of lithium-ion batteries (LIBs) pack/module. In this work, a 25 Ah pouch type Li[Ni0.7Co0.15Mn0.15]O2/graphite LIBs with specific energy of 200 Wh·kg−1 were designed to investigate their thermal behaviors, including temperature distribution, heat generation rate, heat capacity and heat transfer coefficient with environment. Results show that the temperature increment of the charged pouch batteries strongly depends on the discharge rate and depth of discharge. The heat generation rate is mainly influenced by the irreversible heat effect, while the reversible heat is important at all discharge rates and contributes much to the middle evolution of the temperature during discharge, especially at low rate. Subsequently, a prediction model with lumped parameters was used to estimate the temperature evolution at different discharge rates of LIBs. The predicted results match well with the experimental results at all discharge rates. Therefore, the thermal model is suitable to predict the average temperature for the large-scale batteries under normal operating conditions.

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